Software for Agents

Build production-ready infrastructure software that makes AI agents reliable, observable, and safe enough to operate autonomously in high-stakes production environments. Core vision: Create the foundational software layer that AI agents need to work reliably at scale — covering agent orchestration, persistent memory across sessions, tool and API integration, error recovery, observability, safety guardrails, and multi-agent coordination — solving the infrastructure problems that prevent AI agents from being trusted with important autonomous work today. The system must support the full agent infrastructure stack: 1. Reliable agent execution with state persistence and recovery 2. Tool and API integration with retry logic and error handling 3. Memory systems that persist context across sessions 4. Multi-agent coordination and communication protocols 5. Observability: trace every decision, action, and outcome 6. Safety: guardrails that prevent agents from taking dangerous actions Core capabilities: Agent execution runtime: - Durable task execution that survives process restarts and network failures - Checkpointing: save agent state at every step for replay and recovery - Timeout and retry policies configurable per task type - Parallel subtask execution with dependency graph management - Resource usage limits (token budget, API call rate limits, time limits) - Graceful degradation when tools or APIs are unavailable Persistent memory architecture: - Episodic memory: recall what the agent did in past sessions - Semantic memory: vector-indexed knowledge base the agent can query - Working memory: structured context for the current task - Memory compression: summarize old context without losing key facts - Cross-agent memory sharing with access controls - Memory audit trail for debugging agent decisions Tool and API integration layer: - Standardized tool definition format with input/output schemas - Automatic retry with exponential backoff and circuit breakers - Tool capability versioning and backwards compatibility - Sandboxed code execution environment for agent-written code - Browser automation with session persistence - File system access with permissions enforcement Multi-agent coordination: - Agent-to-agent communication protocol with message queuing - Role assignment and task delegation between specialized agents - Consensus mechanisms for decisions requiring multiple agent agreement - Conflict detection when agents take contradictory actions - Orchestrator-worker patterns with supervisor oversight - Shared context injection for agents working on related tasks Observability and debugging: - Full trace of every agent decision with reasoning captured - Tool call logs with inputs, outputs, latencies, and costs - Memory read/write audit trail - Anomaly detection for unexpected agent behavior patterns - Cost tracking per task, session, and customer - Replay any past agent session step-by-step for debugging Safety and guardrails: - Action allowlist/blocklist configurable by deployment context - Human-in-the-loop approval gates for high-risk actions - Sensitive data detection before external API calls - Rate limiting to prevent runaway agents - Irreversibility detection: warn before agents take hard-to-undo actions - Emergency stop mechanisms at task and agent-fleet level Build a working demo covering: 1. Start a multi-step agent task and kill the process mid-execution — show recovery 2. View full trace of an agent's reasoning, tool calls, and decisions 3. Trigger a safety guardrail and show the human approval gate 4. Run two coordinating agents and see their message exchange 5. Query agent memory from a previous session in a new task